Bibliographic record
Abstract
Background: The COVID-19 pandemic has exacerbated socioeconomic deficiencies within Canada's immigrant populations, yet the difference between immigrants from countries with similar language and resources (such as the US) and those from a different background (non-US countries) is not well understood. Accordingly, the IMPACT study at the centre of this article included a Canadian national survey that compared key domains of life in US immigrants with non-US immigrants to provide policymakers with a research-based path toward delivering culturally targeted and socially competent services. Methods: Potential participants were recruited from newcomer support services centers to complete the IMPACT survey which assess participants' self-perceived impacts of COVID-19 on various socioeconomic markers. For each socioeconomic variable, we analyzed the experiential differences between US vs non-US immigrant subgroups. Results: On average, non-US immigrants in Canada were less likely to disclose their COVID-19 health status; this trend was correlated with reported concerns over discontinuation of one’s income. Qualitative themes within the non-US immigrant subgroup elucidated a mentality of “making it on [one’s]own”, and consequently, a reluctance to seek out external resources. Surprisingly, the US immigrant subgroup was subject to a comparatively greater post-pandemic decrease in socioeconomic well-being, resulting in proportionally greater food and financial insecurities than non-US immigrants. Conclusion: The study highlighted two key findings: (1) US immigrants faced a proportionally increased instability of their socioeconomic well-being; whilst (2) non-US immigrants faced greater social and intrapersonal barriers to external supports and experienced a greater incidence of COVID-19 infections, likely resulting from this cohorts reluctance to miss employment income.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".